{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:35:53.527631Z",
     "start_time": "2019-04-29T02:35:50.931181Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import os\n",
    "os.chdir('D:\\\\Give Me Some Credit')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**一般分析流程**"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:35:56.939421Z",
     "start_time": "2019-04-29T02:35:56.487370Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150000 entries, 0 to 149999\n",
      "Data columns (total 12 columns):\n",
      "ID                                      150000 non-null int64\n",
      "SeriousDlqin2yrs                        150000 non-null int64\n",
      "RevolvingUtilizationOfUnsecuredLines    150000 non-null float64\n",
      "age                                     150000 non-null int64\n",
      "NumberOfTime30-59DaysPastDueNotWorse    150000 non-null int64\n",
      "DebtRatio                               150000 non-null float64\n",
      "MonthlyIncome                           120269 non-null float64\n",
      "NumberOfOpenCreditLinesAndLoans         150000 non-null int64\n",
      "NumberOfTimes90DaysLate                 150000 non-null int64\n",
      "NumberRealEstateLoansOrLines            150000 non-null int64\n",
      "NumberOfTime60-89DaysPastDueNotWorse    150000 non-null int64\n",
      "NumberOfDependents                      146076 non-null float64\n",
      "dtypes: float64(4), int64(8)\n",
      "memory usage: 13.7 MB\n"
     ]
    }
   ],
   "source": [
    "T_data=pd.read_csv('cs-training.csv',header=0)\n",
    "T_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:35:59.484810Z",
     "start_time": "2019-04-29T02:35:59.445782Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.766127</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>0.802982</td>\n",
       "      <td>9120.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.957151</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.121876</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.658180</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0.085113</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.233810</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.036050</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0.907239</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0.024926</td>\n",
       "      <td>63588.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0.213179</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0.375607</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0.305682</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>5710.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0.754464</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0.209940</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0.116951</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0.189169</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0.606291</td>\n",
       "      <td>23684.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "ID                                                                \n",
       "1                  1                              0.766127   45   \n",
       "2                  0                              0.957151   40   \n",
       "3                  0                              0.658180   38   \n",
       "4                  0                              0.233810   30   \n",
       "5                  0                              0.907239   49   \n",
       "6                  0                              0.213179   74   \n",
       "7                  0                              0.305682   57   \n",
       "8                  0                              0.754464   39   \n",
       "9                  0                              0.116951   27   \n",
       "10                 0                              0.189169   57   \n",
       "\n",
       "    NumberOfTime30-59DaysPastDueNotWorse    DebtRatio  MonthlyIncome  \\\n",
       "ID                                                                     \n",
       "1                                      2     0.802982         9120.0   \n",
       "2                                      0     0.121876         2600.0   \n",
       "3                                      1     0.085113         3042.0   \n",
       "4                                      0     0.036050         3300.0   \n",
       "5                                      1     0.024926        63588.0   \n",
       "6                                      0     0.375607         3500.0   \n",
       "7                                      0  5710.000000            NaN   \n",
       "8                                      0     0.209940         3500.0   \n",
       "9                                      0    46.000000            NaN   \n",
       "10                                     0     0.606291        23684.0   \n",
       "\n",
       "    NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "ID                                                             \n",
       "1                                13                        0   \n",
       "2                                 4                        0   \n",
       "3                                 2                        1   \n",
       "4                                 5                        0   \n",
       "5                                 7                        0   \n",
       "6                                 3                        0   \n",
       "7                                 8                        0   \n",
       "8                                 8                        0   \n",
       "9                                 2                        0   \n",
       "10                                9                        0   \n",
       "\n",
       "    NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "ID                                                                       \n",
       "1                              6                                     0   \n",
       "2                              0                                     0   \n",
       "3                              0                                     0   \n",
       "4                              0                                     0   \n",
       "5                              1                                     0   \n",
       "6                              1                                     0   \n",
       "7                              3                                     0   \n",
       "8                              0                                     0   \n",
       "9                              0                                     0   \n",
       "10                             4                                     0   \n",
       "\n",
       "    NumberOfDependents  \n",
       "ID                      \n",
       "1                  2.0  \n",
       "2                  1.0  \n",
       "3                  0.0  \n",
       "4                  0.0  \n",
       "5                  0.0  \n",
       "6                  1.0  \n",
       "7                  0.0  \n",
       "8                  0.0  \n",
       "9                  NaN  \n",
       "10                 2.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T_data=T_data.set_index('ID',drop=True) \n",
    "T_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-19T02:01:37.554806Z",
     "start_time": "2019-04-19T02:01:37.549804Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150000, 11)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:36:02.711005Z",
     "start_time": "2019-04-29T02:36:02.679986Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>好坏客户</th>\n",
       "      <th>可用额度比值</th>\n",
       "      <th>年龄</th>\n",
       "      <th>逾期30-59天笔数</th>\n",
       "      <th>负债率</th>\n",
       "      <th>月收入</th>\n",
       "      <th>信贷数量</th>\n",
       "      <th>逾期90天笔数</th>\n",
       "      <th>固定资产贷款量</th>\n",
       "      <th>逾期60-89天笔数</th>\n",
       "      <th>家属数量</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.766127</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>0.802982</td>\n",
       "      <td>9120.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.957151</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.121876</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.658180</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0.085113</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.233810</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.036050</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0.907239</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0.024926</td>\n",
       "      <td>63588.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    好坏客户    可用额度比值  年龄  逾期30-59天笔数       负债率      月收入  信贷数量  逾期90天笔数  固定资产贷款量  \\\n",
       "ID                                                                              \n",
       "1      1  0.766127  45           2  0.802982   9120.0    13        0        6   \n",
       "2      0  0.957151  40           0  0.121876   2600.0     4        0        0   \n",
       "3      0  0.658180  38           1  0.085113   3042.0     2        1        0   \n",
       "4      0  0.233810  30           0  0.036050   3300.0     5        0        0   \n",
       "5      0  0.907239  49           1  0.024926  63588.0     7        0        1   \n",
       "\n",
       "    逾期60-89天笔数  家属数量  \n",
       "ID                    \n",
       "1            0   2.0  \n",
       "2            0   1.0  \n",
       "3            0   0.0  \n",
       "4            0   0.0  \n",
       "5            0   0.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states={'SeriousDlqin2yrs':'好坏客户',\n",
    "        'RevolvingUtilizationOfUnsecuredLines':'可用额度比值',\n",
    "        'age':'年龄',\n",
    "        'NumberOfTime30-59DaysPastDueNotWorse':'逾期30-59天笔数',\n",
    "        'DebtRatio':'负债率',\n",
    "        'MonthlyIncome':'月收入',\n",
    "        'NumberOfOpenCreditLinesAndLoans':'信贷数量',\n",
    "        'NumberOfTimes90DaysLate':'逾期90天笔数',\n",
    "        'NumberRealEstateLoansOrLines':'固定资产贷款量',\n",
    "        'NumberOfTime60-89DaysPastDueNotWorse':'逾期60-89天笔数',\n",
    "        'NumberOfDependents':'家属数量'}\n",
    "T_data.rename(columns=states,inplace=True)\n",
    "T_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 缺失值处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "月收入和家属数量存在缺失，进行填补"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:36:05.384554Z",
     "start_time": "2019-04-29T02:36:05.374548Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "月收入缺失比:19.82%\n"
     ]
    }
   ],
   "source": [
    "print(\"月收入缺失比:{:.2%}\".format(T_data['月收入'].isnull().sum()/T_data.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:36:05.928221Z",
     "start_time": "2019-04-29T02:36:05.918215Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29731"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T_data['月收入'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:36:06.382655Z",
     "start_time": "2019-04-29T02:36:06.373651Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "家属数量缺失比:2.62%\n"
     ]
    }
   ],
   "source": [
    "print(\"家属数量缺失比:{:.2%}\".format(T_data['家属数量'].isnull().sum()/T_data.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:36:06.911423Z",
     "start_time": "2019-04-29T02:36:06.902416Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3924"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T_data['家属数量'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:36:07.490550Z",
     "start_time": "2019-04-29T02:36:07.459533Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(T_data['好坏客户'])-T_data['好坏客户'][T_data['家属数量'].notnull()].sum() \n",
    "#删除家属缺失值存在的行样本，共删除了179个坏客户，3924-179=3745个好客户，对于150000个数量中坏客户10026来说，\n",
    "#删除影响不大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "月收入缺失值用平均值填充，家属数量缺失较少，将缺失的删掉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:36:09.036458Z",
     "start_time": "2019-04-29T02:36:08.959407Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(146076, 11)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T_data=T_data.fillna({'月收入':T_data['月收入'].mean()})\n",
    "df1=T_data.dropna()\n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 异常值处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "都是数值型变量，画出箱线图和直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:36:10.697994Z",
     "start_time": "2019-04-29T02:36:10.692992Z"
    }
   },
   "outputs": [],
   "source": [
    "x1=df1['可用额度比值']\n",
    "x2=df1['负债率']\n",
    "x3=df1[\"年龄\"]\n",
    "x4=df1[\"逾期30-59天笔数\"]\n",
    "x5=df1[\"逾期60-89天笔数\"]\n",
    "x6=df1[\"逾期90天笔数\"]\n",
    "x7=df1[\"信贷数量\"]\n",
    "x8=df1[\"固定资产贷款量\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:36:42.741450Z",
     "start_time": "2019-04-29T02:36:41.080841Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\anaconda3\\lib\\site-packages\\matplotlib\\cbook\\__init__.py:424: MatplotlibDeprecationWarning: \n",
      "Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n",
      "  warn_deprecated(\"2.2\", \"Passing one of 'on', 'true', 'off', 'false' as a \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.font_manager import FontProperties\n",
    "font=FontProperties(fname=r\"C:\\\\Windows\\\\Fonts\\\\msyh.ttf\",size=13)\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "## 箱线图\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.figure(figsize=(20,8)) \n",
    "plt.subplot(231)\n",
    "plt.boxplot(x=x1,patch_artist=True,showmeans=True,boxprops={'color':'black','facecolor':'#9999ff'},\n",
    "           flierprops={'marker':'o','markerfacecolor':'red','color':'black'},meanprops={'marker':'D','markerfacecolor':'indianred'},\n",
    "           medianprops={'linestyle':'--','color':'orange'},vert=True,labels=['可用额度比值'])\n",
    "plt.title('可用额度比值的箱线图')\n",
    "plt.tick_params(top='off',right='off')\n",
    "plt.subplot(232)\n",
    "plt.boxplot(x=x2,patch_artist=True,showmeans=True,boxprops={'color':'black','facecolor':'#9999ff'},\n",
    "           flierprops={'marker':'o','markerfacecolor':'red','color':'black'},meanprops={'marker':'D','markerfacecolor':'indianred'},\n",
    "           medianprops={'linestyle':'--','color':'orange'},vert=True,labels=['负债率'])\n",
    "plt.title('负债率的箱线图')\n",
    "plt.tick_params(top='off',right='off')\n",
    "\n",
    "plt.subplot(233)\n",
    "plt.boxplot(x=x3,patch_artist=True,showmeans=True,boxprops={'color':'black','facecolor':'#9999ff'},\n",
    "           flierprops={'marker':'o','markerfacecolor':'red','color':'black'},meanprops={'marker':'D','markerfacecolor':'indianred'},\n",
    "           medianprops={'linestyle':'--','color':'orange'},vert=True,labels=['年龄'])\n",
    "plt.title('年龄的箱线图')\n",
    "plt.tick_params(top='off',right='off')\n",
    "\n",
    "plt.subplot(234)\n",
    "plt.boxplot(x=[x4,x5,x6],patch_artist=True,showmeans=True,boxprops={'color':'black','facecolor':'#9999ff'},\n",
    "           flierprops={'marker':'o','markerfacecolor':'red','color':'black'},meanprops={'marker':'D','markerfacecolor':'indianred'},\n",
    "           medianprops={'linestyle':'--','color':'orange'},vert=True,labels=['逾期30-59天笔数','逾期60-89天笔数','逾期90天笔数'])\n",
    "plt.title('不同逾期时间的箱线图')\n",
    "plt.tick_params(top='off',right='off')\n",
    "\n",
    "plt.subplot(235)\n",
    "plt.boxplot(x=x7,patch_artist=True,showmeans=True,boxprops={'color':'black','facecolor':'#9999ff'},\n",
    "           flierprops={'marker':'o','markerfacecolor':'red','color':'black'},meanprops={'marker':'D','markerfacecolor':'indianred'},\n",
    "           medianprops={'linestyle':'--','color':'orange'},vert=True,labels=['信贷数量'])\n",
    "plt.title('不同逾期时间的箱线图')\n",
    "plt.tick_params(top='off',right='off')\n",
    "plt.subplot(236)\n",
    "plt.boxplot(x=x8,patch_artist=True,showmeans=True,boxprops={'color':'black','facecolor':'#9999ff'},\n",
    "           flierprops={'marker':'o','markerfacecolor':'red','color':'black'},meanprops={'marker':'D','markerfacecolor':'indianred'},\n",
    "           medianprops={'linestyle':'--','color':'orange'},vert=True,labels=['固定资产贷款量'])\n",
    "plt.title('固定资产贷款量的箱线图')\n",
    "plt.tick_params(top='off',right='off')\n",
    "# plt.tight_layout() #防止图形之间重叠\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "异常值处理消除不合逻辑的数据和超级离群的数据，可用额度比值应该小于1，年龄为0的是异常值，逾期天数笔数大于80的是超级离群数据，固定资产贷款量大于50的是超级离群数据，将这些离群值过滤掉，筛选出剩余部分数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:36:44.812006Z",
     "start_time": "2019-04-29T02:36:44.721947Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(142559, 11)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=df1[df1['可用额度比值']<1]\n",
    "df1=df1[df1['年龄']>0]\n",
    "df1=df1[df1['逾期30-59天笔数']<80]\n",
    "df1=df1[df1['逾期60-89天笔数']<80]\n",
    "df1=df1[df1['逾期90天笔数']<80]\n",
    "df1=df1[df1['固定资产贷款量']<50]\n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:39:08.444547Z",
     "start_time": "2019-04-29T02:39:05.927408Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##直方图\n",
    "from matplotlib.font_manager import FontProperties\n",
    "font=FontProperties(fname=r\"C:\\\\Windows\\\\Fonts\\\\msyh.ttf\",size=13)\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "plt.style.use('ggplot')\n",
    "# plt.style.use('seaborn-whitegrid')\n",
    "fig=plt.figure(figsize=(20,8)) #调整图形大小\n",
    "plt.subplot2grid((2,4),(0,0))\n",
    "x1.plot(kind='hist',edgecolor='black')# 直方图 \n",
    "plt.xlabel('可用额度比值')\n",
    "plt.title(\"可用额度比值分布情况\") # 标题\n",
    "\n",
    "plt.subplot2grid((2,4),(0,1))\n",
    "x2.plot(kind=\"hist\",edgecolor='black')\n",
    "plt.xlabel('负债率')\n",
    "plt.title(\"负债率分布情况\")\n",
    "\n",
    "plt.subplot2grid((2,4),(0,2))\n",
    "x3.plot(kind=\"hist\",edgecolor='black')\n",
    "plt.xlabel('年龄')\n",
    "plt.title(\"年龄分布情况\")\n",
    "\n",
    "plt.subplot2grid((2,4),(0,3))\n",
    "x4.plot(kind=\"hist\",edgecolor='black')\n",
    "plt.xlabel('逾期30-59天笔数')\n",
    "plt.title(\"逾期30-59天笔数分布情况\")\n",
    "\n",
    "plt.subplot2grid((2,4),(1,0))\n",
    "x5.plot(kind=\"hist\",edgecolor='black')\n",
    "plt.xlabel('逾期60-89天笔数')\n",
    "plt.title(\"逾期60-89天笔数分布情况\")\n",
    "\n",
    "plt.subplot2grid((2,4),(1,1))\n",
    "x6.plot(kind=\"hist\",edgecolor='black')\n",
    "plt.xlabel('逾期90天笔数')\n",
    "plt.title(\"逾期90天笔数分布情况\")\n",
    "\n",
    "plt.subplot2grid((2,4),(1,2))\n",
    "x7.plot(kind=\"hist\",edgecolor='black')\n",
    "plt.xlabel('信贷数量')\n",
    "plt.title(\"信贷数量分布情况\")\n",
    "\n",
    "plt.subplot2grid((2,4),(1,3))\n",
    "x8.plot(kind=\"hist\",edgecolor='black')\n",
    "plt.xlabel('固定资产贷数量')\n",
    "plt.title(\"固定资产贷数量分布情况\")\n",
    "plt.tight_layout() #防止图形之间重叠\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探索性分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 单变量分析\n",
    "是分析一个自变量和因变量之间的联系，此处以年龄和好坏客户为例进行分析。\n",
    "将年龄均分成5组，求出每组的总的用户数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:39:29.312685Z",
     "start_time": "2019-04-29T02:39:29.202610Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "年龄\n",
       "(20.914, 38.2]    26984\n",
       "(38.2, 55.4]      56660\n",
       "(55.4, 72.6]      45709\n",
       "(72.6, 89.8]      12640\n",
       "(89.8, 107.0]       566\n",
       "Name: 好坏客户, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_cut=pd.cut(df1['年龄'],5)\n",
    "age_cut_group=df1['好坏客户'].groupby(age_cut).count()\n",
    "age_cut_group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "求各组的坏客户数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-29T02:39:34.260919Z",
     "start_time": "2019-04-29T02:39:34.238903Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "年龄\n",
       "(20.914, 38.2]    2478\n",
       "(38.2, 55.4]      4067\n",
       "(55.4, 72.6]      1664\n",
       "(72.6, 89.8]       273\n",
       "(89.8, 107.0]       12\n",
       "Name: 好坏客户, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_cut_grouped1=df1[\"好坏客户\"].groupby(age_cut).sum()\n",
    "age_cut_grouped1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:29:49.329784Z",
     "start_time": "2019-04-04T06:29:49.292784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>总客户数</th>\n",
       "      <th>坏客户数</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年龄</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>(20.914, 38.2]</th>\n",
       "      <td>26984</td>\n",
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       "    <tr>\n",
       "      <th>(38.2, 55.4]</th>\n",
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       "    <tr>\n",
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       "      <td>45709</td>\n",
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       "      <td>566</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 总客户数  坏客户数\n",
       "年龄                         \n",
       "(20.914, 38.2]  26984  2478\n",
       "(38.2, 55.4]    56660  4067\n",
       "(55.4, 72.6]    45709  1664\n",
       "(72.6, 89.8]    12640   273\n",
       "(89.8, 107.0]     566    12"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=pd.merge(pd.DataFrame(age_cut_group),pd.DataFrame(age_cut_grouped1),left_index=True,right_index=True)\n",
    "df2.rename(columns={'好坏客户_x':'总客户数','好坏客户_y':'坏客户数'},inplace=True)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:29:49.717784Z",
     "start_time": "2019-04-04T06:29:49.703784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>总客户数</th>\n",
       "      <th>坏客户数</th>\n",
       "      <th>好客户数</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年龄</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(20.914, 38.2]</th>\n",
       "      <td>26984</td>\n",
       "      <td>2478</td>\n",
       "      <td>24506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(38.2, 55.4]</th>\n",
       "      <td>56660</td>\n",
       "      <td>4067</td>\n",
       "      <td>52593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(55.4, 72.6]</th>\n",
       "      <td>45709</td>\n",
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       "    <tr>\n",
       "      <th>(72.6, 89.8]</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>(89.8, 107.0]</th>\n",
       "      <td>566</td>\n",
       "      <td>12</td>\n",
       "      <td>554</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 总客户数  坏客户数   好客户数\n",
       "年龄                                \n",
       "(20.914, 38.2]  26984  2478  24506\n",
       "(38.2, 55.4]    56660  4067  52593\n",
       "(55.4, 72.6]    45709  1664  44045\n",
       "(72.6, 89.8]    12640   273  12367\n",
       "(89.8, 107.0]     566    12    554"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.insert(2,\"好客户数\",df2[\"总客户数\"]-df2[\"坏客户数\"])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:29:50.261784Z",
     "start_time": "2019-04-04T06:29:50.244784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>总客户数</th>\n",
       "      <th>坏客户数</th>\n",
       "      <th>坏客户占比</th>\n",
       "      <th>好客户数</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年龄</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(20.914, 38.2]</th>\n",
       "      <td>26984</td>\n",
       "      <td>2478</td>\n",
       "      <td>0.091832</td>\n",
       "      <td>24506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(38.2, 55.4]</th>\n",
       "      <td>56660</td>\n",
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       "      <td>52593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(55.4, 72.6]</th>\n",
       "      <td>45709</td>\n",
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       "      <td>12640</td>\n",
       "      <td>273</td>\n",
       "      <td>0.021598</td>\n",
       "      <td>12367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(89.8, 107.0]</th>\n",
       "      <td>566</td>\n",
       "      <td>12</td>\n",
       "      <td>0.021201</td>\n",
       "      <td>554</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 总客户数  坏客户数     坏客户占比   好客户数\n",
       "年龄                                          \n",
       "(20.914, 38.2]  26984  2478  0.091832  24506\n",
       "(38.2, 55.4]    56660  4067  0.071779  52593\n",
       "(55.4, 72.6]    45709  1664  0.036404  44045\n",
       "(72.6, 89.8]    12640   273  0.021598  12367\n",
       "(89.8, 107.0]     566    12  0.021201    554"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.insert(2,\"坏客户占比\",df2[\"坏客户数\"]/df2[\"总客户数\"])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:29:50.743784Z",
     "start_time": "2019-04-04T06:29:50.734784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalIndex([(20.914, 38.2], (38.2, 55.4], (55.4, 72.6], (72.6, 89.8],\n",
       "                  (89.8, 107.0]],\n",
       "                 categories=[(20.914, 38.2], (38.2, 55.4], (55.4, 72.6], (72.6, 89.8], (89.8, 107.0]], ordered=True, name='年龄', dtype='category')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:29:51.526784Z",
     "start_time": "2019-04-04T06:29:51.244784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '年龄与好坏客户数分布图')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('ggplot')\n",
    "# fig=plt.figure(figsize=(10,8))\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "# df2[[\"好客户数\",\"坏客户数\"]].plot(kind=\"bar\",edgecolor='black')\n",
    "ax1=df2[[\"好客户数\",\"坏客户数\"]].plot(kind=\"bar\",edgecolor='black',figsize=(10,5))\n",
    "ax1.set_xticklabels(df2.index,rotation=15)\n",
    "ax1.set_ylabel(\"客户数\")\n",
    "ax1.set_title(\"年龄与好坏客户数分布图\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:29:52.019784Z",
     "start_time": "2019-04-04T06:29:51.764784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '坏客户率随年龄的变化趋势图')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax11=df2[\"坏客户占比\"].plot(figsize=(10,5))\n",
    "ax11.set_xticklabels([0,20,29,38,47,55,64,72,81,89,98,107])\n",
    "ax11.set_ylabel(\"坏客户率\")\n",
    "ax11.set_title(\"坏客户率随年龄的变化趋势图\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "可以看出随着年龄的增长，坏客户率在降低，其中38~55之间变化幅度最大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 多变量分析\n",
    "多变量分析就是对各个变量之间的相关性进行探索，线性回归模型中的特征之间由于存在精确相关关系或高度相关关系而使模型估计失真或难以估计准确，相关系数为1或者-1变量之间的相关性最大，对于相关性大的两组变量可以择一处理\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:29:58.736784Z",
     "start_time": "2019-04-04T06:29:54.581784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "corr = df1.corr()#计算各变量的相关性系数\n",
    "xticks = list(corr.index)#x轴标签\n",
    "yticks = list(corr.index)#y轴标签\n",
    "fig = plt.figure(figsize=(15,10))\n",
    "ax1 = fig.add_subplot(1, 1, 1)\n",
    "sns.heatmap(corr, annot=True, cmap=\"rainbow\",ax=ax1,linewidths=.5, annot_kws={'size': 9, 'weight': 'bold', 'color': 'blue'})\n",
    "ax1.set_xticklabels(xticks, rotation=45, fontsize=15)\n",
    "ax1.set_yticklabels(yticks, rotation=0, fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到各变量之间的相关性比较小，所以不需要操作，一般相关系数大于0.6可以进行变量剔除。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征选择\n",
    "**这里使用IV值进行特征选择**\n",
    "- **WOE分箱**\n",
    "- **WOE值计算**\n",
    "- **IV值计算**\n",
    "- **WOE值替换**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WOE分箱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:30:02.672784Z",
     "start_time": "2019-04-04T06:30:02.531784Z"
    }
   },
   "outputs": [],
   "source": [
    "cut1=pd.qcut(df1[\"可用额度比值\"],4,labels=False)\n",
    "cut2=pd.qcut(df1[\"年龄\"],8,labels=False)\n",
    "bins3=[-1,0,1,3,5,13]\n",
    "cut3=pd.cut(df1[\"逾期30-59天笔数\"],bins3,labels=False)\n",
    "cut4=pd.qcut(df1[\"负债率\"],3,labels=False)\n",
    "cut5=pd.qcut(df1[\"月收入\"],4,labels=False)\n",
    "cut6=pd.qcut(df1[\"信贷数量\"],4,labels=False)\n",
    "bins7=[-1, 0, 1, 3,5, 20]\n",
    "cut7=pd.cut(df1[\"逾期90天笔数\"],bins7,labels=False)\n",
    "bins8=[-1, 0,1,2, 3, 33]\n",
    "cut8=pd.cut(df1[\"固定资产贷款量\"],bins8,labels=False)\n",
    "bins9=[-1, 0, 1, 3, 12]\n",
    "cut9=pd.cut(df1[\"逾期60-89天笔数\"],bins9,labels=False)\n",
    "bins10=[-1, 0, 1, 2, 3, 5, 21]\n",
    "cut10=pd.cut(df1[\"家属数量\"],bins10,labels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:30:03.044784Z",
     "start_time": "2019-04-04T06:30:03.037784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    35640\n",
       "1    35640\n",
       "0    35640\n",
       "2    35639\n",
       "Name: 可用额度比值, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cut1.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WOE计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:30:04.398784Z",
     "start_time": "2019-04-04T06:30:04.137784Z"
    }
   },
   "outputs": [],
   "source": [
    "rate=df1[\"好坏客户\"].sum()/(df1[\"好坏客户\"].count()-df1[\"好坏客户\"].sum())\n",
    "def get_woe_data(cut):\n",
    "    grouped=df1[\"好坏客户\"].groupby(cut,as_index = True).value_counts()\n",
    "    woe=np.log(grouped.unstack().iloc[:,1]/grouped.unstack().iloc[:,0]/rate)\n",
    "    return woe\n",
    "cut1_woe=get_woe_data(cut1)\n",
    "cut2_woe=get_woe_data(cut2)\n",
    "cut3_woe=get_woe_data(cut3)\n",
    "cut4_woe=get_woe_data(cut4)\n",
    "cut5_woe=get_woe_data(cut5)\n",
    "cut6_woe=get_woe_data(cut6)\n",
    "cut7_woe=get_woe_data(cut7)\n",
    "cut8_woe=get_woe_data(cut8)\n",
    "cut9_woe=get_woe_data(cut9)\n",
    "cut10_woe=get_woe_data(cut10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:30:04.707784Z",
     "start_time": "2019-04-04T06:30:04.700784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "可用额度比值\n",
       "0   -1.203637\n",
       "1   -1.135436\n",
       "2   -0.247395\n",
       "3    1.044468\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cut1_woe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "随便挑几个变量看下woe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:30:05.884784Z",
     "start_time": "2019-04-04T06:30:05.706784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14918518>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cut1_woe.plot.bar(color='b',alpha=0.5,rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:30:06.507784Z",
     "start_time": "2019-04-04T06:30:06.282784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14970a20>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cut2_woe.plot.bar(color='b',alpha=0.5,rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:30:07.046784Z",
     "start_time": "2019-04-04T06:30:06.884784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13fe18d0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cut3_woe.plot.bar(color='b',alpha=0.5,rot=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出woe已调整到具有单调性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IV值计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:30:09.528784Z",
     "start_time": "2019-04-04T06:30:09.067784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'IV')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_IV_data(cut,cut_woe):\n",
    "    grouped=df1[\"好坏客户\"].groupby(cut,as_index = True).value_counts()\n",
    "    cut_IV=((grouped.unstack().iloc[:,1]/df1[\"好坏客户\"].sum()-grouped.unstack().iloc[:,0]/(df1[\"好坏客户\"].count()-df1[\"好坏客户\"].sum()))*cut_woe).sum()    \n",
    "    return cut_IV\n",
    "#计算各分组的IV值\n",
    "cut1_IV=get_IV_data(cut1,cut1_woe)\n",
    "cut2_IV=get_IV_data(cut2,cut2_woe)\n",
    "cut3_IV=get_IV_data(cut3,cut3_woe)\n",
    "cut4_IV=get_IV_data(cut4,cut4_woe)\n",
    "cut5_IV=get_IV_data(cut5,cut5_woe)\n",
    "cut6_IV=get_IV_data(cut6,cut6_woe)\n",
    "cut7_IV=get_IV_data(cut7,cut7_woe)\n",
    "cut8_IV=get_IV_data(cut8,cut8_woe)\n",
    "cut9_IV=get_IV_data(cut9,cut9_woe)\n",
    "cut10_IV=get_IV_data(cut10,cut10_woe)\n",
    "IV=pd.DataFrame([cut1_IV,cut2_IV,cut3_IV,cut4_IV,cut5_IV,cut6_IV,cut7_IV,cut8_IV,cut9_IV,cut10_IV],index=['可用额度比值','年龄','逾期30-59天笔数','负债率','月收入','信贷数量','逾期90天笔数','固定资产贷款量','逾期60-89天笔数','家属数量'],columns=['IV'])\n",
    "iv=IV.plot.bar(color='b',alpha=0.3,rot=30,figsize=(10,5),fontsize=(10))\n",
    "iv.set_title('特征变量与IV值分布图',fontsize=(15))\n",
    "iv.set_xlabel('特征变量',fontsize=(15))\n",
    "iv.set_ylabel('IV',fontsize=(15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:30:09.671784Z",
     "start_time": "2019-04-04T06:30:09.663784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>可用额度比值</th>\n",
       "      <td>0.867677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年龄</th>\n",
       "      <td>0.226285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>逾期30-59天笔数</th>\n",
       "      <td>0.638915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>负债率</th>\n",
       "      <td>0.021881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>月收入</th>\n",
       "      <td>0.056417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>信贷数量</th>\n",
       "      <td>0.035331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>逾期90天笔数</th>\n",
       "      <td>0.780782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>固定资产贷款量</th>\n",
       "      <td>0.052406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>逾期60-89天笔数</th>\n",
       "      <td>0.497545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>家属数量</th>\n",
       "      <td>0.033580</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  IV\n",
       "可用额度比值      0.867677\n",
       "年龄          0.226285\n",
       "逾期30-59天笔数  0.638915\n",
       "负债率         0.021881\n",
       "月收入         0.056417\n",
       "信贷数量        0.035331\n",
       "逾期90天笔数     0.780782\n",
       "固定资产贷款量     0.052406\n",
       "逾期60-89天笔数  0.497545\n",
       "家属数量        0.033580"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一般选取IV大于0.02的特征变量进行后续训练，从以上可以看出所有变量均满足，所以选取全部的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WOE值替换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:30:12.106784Z",
     "start_time": "2019-04-04T06:30:11.859784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>好坏客户</th>\n",
       "      <th>可用额度比值</th>\n",
       "      <th>年龄</th>\n",
       "      <th>逾期30-59天笔数</th>\n",
       "      <th>负债率</th>\n",
       "      <th>月收入</th>\n",
       "      <th>信贷数量</th>\n",
       "      <th>逾期90天笔数</th>\n",
       "      <th>固定资产贷款量</th>\n",
       "      <th>逾期60-89天笔数</th>\n",
       "      <th>家属数量</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.044468</td>\n",
       "      <td>0.246969</td>\n",
       "      <td>1.701683</td>\n",
       "      <td>0.191066</td>\n",
       "      <td>-0.321369</td>\n",
       "      <td>0.045474</td>\n",
       "      <td>-0.345338</td>\n",
       "      <td>0.665870</td>\n",
       "      <td>-0.244603</td>\n",
       "      <td>0.197472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1.044468</td>\n",
       "      <td>0.316890</td>\n",
       "      <td>-0.476517</td>\n",
       "      <td>-0.151014</td>\n",
       "      <td>0.289489</td>\n",
       "      <td>0.222781</td>\n",
       "      <td>-0.345338</td>\n",
       "      <td>0.197418</td>\n",
       "      <td>-0.244603</td>\n",
       "      <td>0.097683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1.044468</td>\n",
       "      <td>0.316890</td>\n",
       "      <td>0.899640</td>\n",
       "      <td>-0.151014</td>\n",
       "      <td>0.289489</td>\n",
       "      <td>0.222781</td>\n",
       "      <td>1.990279</td>\n",
       "      <td>0.197418</td>\n",
       "      <td>-0.244603</td>\n",
       "      <td>-0.150675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.247395</td>\n",
       "      <td>0.518166</td>\n",
       "      <td>-0.476517</td>\n",
       "      <td>-0.151014</td>\n",
       "      <td>0.289489</td>\n",
       "      <td>0.222781</td>\n",
       "      <td>-0.345338</td>\n",
       "      <td>0.197418</td>\n",
       "      <td>-0.244603</td>\n",
       "      <td>-0.150675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1.044468</td>\n",
       "      <td>0.166515</td>\n",
       "      <td>0.899640</td>\n",
       "      <td>-0.151014</td>\n",
       "      <td>-0.321369</td>\n",
       "      <td>-0.255957</td>\n",
       "      <td>-0.345338</td>\n",
       "      <td>-0.235787</td>\n",
       "      <td>-0.244603</td>\n",
       "      <td>-0.150675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    好坏客户    可用额度比值        年龄  逾期30-59天笔数       负债率       月收入      信贷数量  \\\n",
       "ID                                                                       \n",
       "1      1  1.044468  0.246969    1.701683  0.191066 -0.321369  0.045474   \n",
       "2      0  1.044468  0.316890   -0.476517 -0.151014  0.289489  0.222781   \n",
       "3      0  1.044468  0.316890    0.899640 -0.151014  0.289489  0.222781   \n",
       "4      0 -0.247395  0.518166   -0.476517 -0.151014  0.289489  0.222781   \n",
       "5      0  1.044468  0.166515    0.899640 -0.151014 -0.321369 -0.255957   \n",
       "\n",
       "     逾期90天笔数   固定资产贷款量  逾期60-89天笔数      家属数量  \n",
       "ID                                            \n",
       "1  -0.345338  0.665870   -0.244603  0.197472  \n",
       "2  -0.345338  0.197418   -0.244603  0.097683  \n",
       "3   1.990279  0.197418   -0.244603 -0.150675  \n",
       "4  -0.345338  0.197418   -0.244603 -0.150675  \n",
       "5  -0.345338 -0.235787   -0.244603 -0.150675  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new=pd.DataFrame()   #新建df_new存放woe转换后的数据\n",
    "def replace_data(cut,cut_woe):\n",
    "    a=[]\n",
    "    for i in cut.unique():\n",
    "        a.append(i)\n",
    "        a.sort()\n",
    "    for m in range(len(a)):\n",
    "        cut.replace(a[m],cut_woe.values[m],inplace=True)\n",
    "    return cut\n",
    "df_new[\"好坏客户\"]=df1[\"好坏客户\"]\n",
    "df_new[\"可用额度比值\"]=replace_data(cut1,cut1_woe)\n",
    "df_new[\"年龄\"]=replace_data(cut2,cut2_woe)\n",
    "df_new[\"逾期30-59天笔数\"]=replace_data(cut3,cut3_woe)\n",
    "df_new[\"负债率\"]=replace_data(cut4,cut4_woe)\n",
    "df_new[\"月收入\"]=replace_data(cut5,cut5_woe)\n",
    "df_new[\"信贷数量\"]=replace_data(cut6,cut6_woe)\n",
    "df_new[\"逾期90天笔数\"]=replace_data(cut7,cut7_woe)\n",
    "df_new[\"固定资产贷款量\"]=replace_data(cut8,cut8_woe)\n",
    "df_new[\"逾期60-89天笔数\"]=replace_data(cut9,cut9_woe)\n",
    "df_new[\"家属数量\"]=replace_data(cut10,cut10_woe)\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "信用评分卡主要使用的算法模型是逻辑回归。logistic模型客群变化的敏感度不如其他高复杂度模型，因此稳健更好，鲁棒性更强。另外，模型直观，系数含义好阐述、易理解，使用逻辑回归优点是可以得到一个变量之间的线性关系式和对应的特征权值，方便后面将其转成一一对应的分数形式。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:30:16.232784Z",
     "start_time": "2019-04-04T06:30:14.112784Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "d:\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试成绩：0.9420127197904976\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "x=df_new.iloc[:,1:]\n",
    "y=df_new.iloc[:,:1]\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.6,random_state=100,stratify=y)\n",
    "model=LogisticRegression()\n",
    "clf=model.fit(x_train,y_train)\n",
    "print('测试成绩：{}'.format(clf.score(x_test,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "求特征权值系数coe，后面训练结果转分值时会用到："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:30:17.968784Z",
     "start_time": "2019-04-04T06:30:17.953784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:30:18.551784Z",
     "start_time": "2019-04-04T06:30:18.546784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.61412227, 0.41802156, 0.57438791, 1.11447783, 0.45374369,\n",
       "        0.17471114, 0.58831607, 0.8187892 , 0.44824357, 0.34463511]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coe=clf.coef_        #特征权值系数，后面转换为打分规则时会用到\n",
    "coe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:30:19.091784Z",
     "start_time": "2019-04-04T06:30:19.077784Z"
    }
   },
   "outputs": [],
   "source": [
    "# y_pred=clf.predict(x_test) #直接预测label为0或者为1\n",
    "y_prob=clf.predict_proba(x_test) #预测出为0和为1的概率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:30:19.720784Z",
     "start_time": "2019-04-04T06:30:19.709784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.88649914, 0.11350086],\n",
       "       [0.91376342, 0.08623658],\n",
       "       [0.98814305, 0.01185695],\n",
       "       ...,\n",
       "       [0.97985431, 0.02014569],\n",
       "       [0.27433529, 0.72566471],\n",
       "       [0.98009016, 0.01990984]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:30:20.377784Z",
     "start_time": "2019-04-04T06:30:20.362784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.11350086, 0.08623658, 0.01185695, ..., 0.02014569, 0.72566471,\n",
       "       0.01990984])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_prob[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:30:21.159784Z",
     "start_time": "2019-04-04T06:30:21.093784Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_pred' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-54-3aaf935e6aec>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0my_pred\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'y_pred' is not defined"
     ]
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型评估\n",
    "模型评估主要看AUC和K-S值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:30:22.685784Z",
     "start_time": "2019-04-04T06:30:22.475784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "fpr, tpr, threshold = roc_curve(y_test, y_prob[:,1])\n",
    "roc_auc = auc(fpr, tpr)\n",
    "plt.plot(fpr, tpr, color='darkorange',label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1.01], [0, 1.01], color='navy',  linestyle='--')\n",
    "plt.xlim([0.0, 1.01])\n",
    "plt.ylim([0.0, 1.01])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC_curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "right_index = list(np.array(tpr)- np.array(fpr))\n",
    "# yuzhi = max(right_index)\n",
    "# index = right_index.index(yuzhi)\n",
    "index=np.argmax(right_index)\n",
    "tpr_val = tpr[index]\n",
    "fpr_val = fpr[index]\n",
    "plt.plot(fpr_val,tpr_val,'*',color='red',markersize=7)\n",
    "show_text=str(round(max(right_index),3))+'('+str(round(fpr_val,3))+','+str(round(tpr_val,3))+')'\n",
    "plt.annotate(show_text,xytext=(fpr_val-0.09,tpr_val+0.05),xy=(fpr_val,tpr_val))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:31:33.246784Z",
     "start_time": "2019-04-04T06:31:33.226784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8436942711378628"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最优阈值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:31:34.680784Z",
     "start_time": "2019-04-04T06:31:34.666784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5379854091351569"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(right_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:32:20.966784Z",
     "start_time": "2019-04-04T06:32:20.948784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.96342986, 0.96342986, 0.95601587, ..., 0.00631866, 0.00619858,\n",
       "       0.00606105])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:30:56.708784Z",
     "start_time": "2019-04-04T06:30:56.498784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x1440 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(1 - threshold, tpr, label='tpr') # ks曲线要按照预测概率降序排列，所以需要1-threshold镜像\n",
    "ax.plot(1 - threshold, fpr, label='fpr')\n",
    "ax.plot(1 - threshold, tpr-fpr,label='KS')\n",
    "plt.xlabel('score')\n",
    "plt.title('KS Curve')\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.figure(figsize=(20,20))\n",
    "legend = ax.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:31:25.363784Z",
     "start_time": "2019-04-04T06:31:25.341784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5379854091351569"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(tpr-fpr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ROC:0.84， K-S:0.53左右，建模效果一般"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型结果转评分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T06:32:04.119784Z",
     "start_time": "2019-04-04T06:32:04.084784Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "可用额度比值对应的分数:[-21.0, -20.0, -4.0, 19.0]\n",
      "年龄对应的分数:[6.0, 4.0, 3.0, 2.0, -0.0, -5.0, -10.0, -12.0]\n",
      "逾期30-59天笔数对应的分数:[-8.0, 15.0, 28.0, 39.0, 44.0]\n",
      "负债率对应的分数:[-5.0, -2.0, 6.0]\n",
      "月收入对应的分数:[4.0, 1.0, -2.0, -4.0]\n",
      "信贷数量对应的分数:[1.0, -1.0, -1.0, 0.0]\n",
      "逾期90天笔数对应的分数:[-6.0, 34.0, 47.0, 56.0, 56.0]\n",
      "固定资产贷款量对应的分数:[5.0, -6.0, -3.0, 2.0, 16.0]\n",
      "逾期60-89天笔数对应的分数:[-3.0, 24.0, 35.0, 39.0]\n",
      "家属数量对应的分数:[-1.0, 1.0, 2.0, 3.0, 4.0, 7.0]\n"
     ]
    }
   ],
   "source": [
    "factor = 20 / np.log(2)\n",
    "offset = 600 - 20 * np.log(20) / np.log(2)\n",
    "def get_score(coe,woe,factor):\n",
    "    scores=[]\n",
    "    for w in woe:\n",
    "        score=round(coe*w*factor,0)\n",
    "        scores.append(score)\n",
    "    return scores\n",
    "x1 = get_score(coe[0][0], cut1_woe, factor)\n",
    "x2 = get_score(coe[0][1], cut2_woe, factor)\n",
    "x3 = get_score(coe[0][2], cut3_woe, factor)\n",
    "x4 = get_score(coe[0][3], cut4_woe, factor)\n",
    "x5 = get_score(coe[0][4], cut5_woe, factor)\n",
    "x6 = get_score(coe[0][5], cut6_woe, factor)\n",
    "x7 = get_score(coe[0][6], cut7_woe, factor)\n",
    "x8 = get_score(coe[0][7], cut8_woe, factor)\n",
    "x9 = get_score(coe[0][8], cut9_woe, factor)\n",
    "x10 = get_score(coe[0][9], cut10_woe, factor)\n",
    "print(\"可用额度比值对应的分数:{}\".format(x1))\n",
    "print(\"年龄对应的分数:{}\".format(x2))\n",
    "print(\"逾期30-59天笔数对应的分数:{}\".format(x3))\n",
    "print(\"负债率对应的分数:{}\".format(x4))\n",
    "print(\"月收入对应的分数:{}\".format(x5))\n",
    "print(\"信贷数量对应的分数:{}\".format(x6))\n",
    "print(\"逾期90天笔数对应的分数:{}\".format(x7))\n",
    "print(\"固定资产贷款量对应的分数:{}\".format(x8))\n",
    "print(\"逾期60-89天笔数对应的分数:{}\".format(x9))\n",
    "print(\"家属数量对应的分数:{}\".format(x10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 计算用户总分\n",
    "## 取自动分箱的边界分割点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T07:25:48.831503Z",
     "start_time": "2019-04-04T07:25:48.744511Z"
    }
   },
   "outputs": [],
   "source": [
    "cu1=pd.qcut(df1[\"可用额度比值\"],4,labels=False,retbins=True)\n",
    "bins1=cu1[1]\n",
    "cu2=pd.qcut(df1[\"年龄\"],8,labels=False,retbins=True)\n",
    "bins2=cu2[1]\n",
    "\n",
    "# bins3=[-1,0,1,3,5,13]\n",
    "# cut3=pd.cut(df1[\"逾期30-59天笔数\"],bins3,labels=False)\n",
    "cu4=pd.qcut(df1[\"负债率\"],3,labels=False,retbins=True)\n",
    "bins4=cu4[1]\n",
    "cu5=pd.qcut(df1[\"月收入\"],4,labels=False,retbins=True)\n",
    "bins5=cu5[1]\n",
    "cu6=pd.qcut(df1[\"信贷数量\"],4,labels=False,retbins=True)\n",
    "bins6=cu6[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 各变量对应的分数求和，算出每个用户的总分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T07:25:51.905195Z",
     "start_time": "2019-04-04T07:25:51.889197Z"
    }
   },
   "outputs": [],
   "source": [
    "def compute_score(series,bins,score):\n",
    "    list = []\n",
    "    i = 0\n",
    "    while i < len(series):\n",
    "        value = series[i]\n",
    "        j = len(bins) - 2\n",
    "        m = len(bins) - 2\n",
    "        while j >= 0:\n",
    "            if value >= bins[j]:\n",
    "                j = -1\n",
    "            else:\n",
    "                j -= 1\n",
    "                m -= 1\n",
    "        list.append(score[m])\n",
    "        i += 1\n",
    "    return list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T07:25:53.500036Z",
     "start_time": "2019-04-04T07:25:53.202066Z"
    }
   },
   "outputs": [],
   "source": [
    "test1=pd.read_csv('cs-test.csv',header=0)\n",
    "test2=test1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T07:26:05.137872Z",
     "start_time": "2019-04-04T07:25:54.112975Z"
    }
   },
   "outputs": [],
   "source": [
    "test1['x1'] = pd.Series(compute_score(test1['RevolvingUtilizationOfUnsecuredLines'], bins1, x1))\n",
    "test1['x2'] = pd.Series(compute_score(test1['age'], bins2, x2))\n",
    "test1['x3'] = pd.Series(compute_score(test1['NumberOfTime30-59DaysPastDueNotWorse'], bins3, x3))\n",
    "test1['x4'] = pd.Series(compute_score(test1['DebtRatio'], bins4, x4))\n",
    "test1['x5'] = pd.Series(compute_score(test1['MonthlyIncome'], bins5, x5))\n",
    "test1['x6'] = pd.Series(compute_score(test1['NumberOfOpenCreditLinesAndLoans'], bins6, x6))\n",
    "test1['x7'] = pd.Series(compute_score(test1['NumberOfTimes90DaysLate'], bins7, x7))\n",
    "test1['x8'] = pd.Series(compute_score(test1['NumberRealEstateLoansOrLines'], bins8, x8))\n",
    "test1['x9'] = pd.Series(compute_score(test1['NumberOfTime60-89DaysPastDueNotWorse'], bins9, x9))\n",
    "test1['x10'] = pd.Series(compute_score(test1['NumberOfDependents'], bins10, x10))\n",
    "test1['Score'] = test1['x1']+test1['x2']+test1['x3']+test1['x4']+test1['x5']+test1['x6']+test1['x7']+test1['x8']+test1['x9']+test1['x10']+600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-04T07:26:07.409645Z",
     "start_time": "2019-04-04T07:26:05.625823Z"
    }
   },
   "outputs": [],
   "source": [
    "test1.to_csv('测试集用户得分表.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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